Tag: Proactive Recommendations

Push notifications are increasingly being used to engage mobile device users with app content. News organizations deliver breaking-news notifications, social platforms inform about new content, games inform about status updates game, etc … with the goal of making the user engage with the service.

In this research, we – Kostadin Kushlev, University of Virginia, Bruno Cardoso, KU Leuven, and myself – explored to what extent users’ current affect, that is, how they are feeling, impacts user engagement. To this end, we analyzed data from a study conducted by Telefónica Research where the participants (N = 337) downloaded a custom-developed app that delivered notifications.

After attending to a notification (N = 32,704), participants reported how they felt in a mini questionnaire. Besides asking how the participants felt, the questionnaire also offered them to voluntarily engage with further content. Participants were not aware that we our main interest was in observing their interaction with said content — they believed that it was mainly there as a courtesy to make their participation in the study more fun.

Participants always had two choices: a mentally demanding and a simple/diverting task. The tasks in these groups were chosen from a list of 4 options each. The mentally demanding offers included: browsing trending games on Google Play, reading the Wikipedia article of the day, filling out a personality questionnaire, or playing a thinking game. The simple and diverting option included watching a trending video, reading fun facts, playing an action game, and watching trending gif images.

The results show a clear impact of affect on the choice of the content:

When feeling good, people tend to avoid mentally demanding tasks. Hence, proactive recommendations for content that requires mental effort should target moments of neutral or even negative valence.

When tense, people tend to avoid diverting tasks. Thus, people who want to reduce task-induced stress might want to rely on external timers to schedule regular breaks with fun activities.

When energetic, people tend to avoid suggestions for further distraction altogether. Hence, proactive recommendations should target moments of low energetic arousal, such as moments of boredom.

These findings show that the current emotional state affects the kind of content users choose to engage with. Future “smart” devices should not only be technologically smart, but also psychologically smart. They should strive to understand how users feel in order to engage them with the most appropriate content at the most opportune of times.

The business model of many internet-service companies is primarily build around your attention: they offer best-in-class services for free in exchange for the users’ eyeballs, i.e. them paying attention to the contents of the services they offer. They pay for their expenses and generate revenue by selling the attracted attention to companies and individuals who’d like to promote their content.

In this battle, we may be facing the tragedy of the commons: when individual companies behave rationally according to their self-interest by increasing their attempts to seek people’s attention, they behave contrary to the best interests of the whole group by depleting the attentional resources of the user and risk that people develop notification blindness (as an analogy to banner blindness).

Attention is not always scarce

However, attention is not always scarce. For example, when people are bored, attention is abundant, and people often turn to their phones to kill time.

Boredom-Triggered Proactive Recommendations

This finding opens the door to using boredom as a content-independent trigger for proactive recommendations. Assuming that proactive recommendations delivered via mobile phone notifications will become more common in the future, using boredom as trigger will benefit service providers as well as the end users:
End users will receive fewer recommendations that are triggered during times when they are busy. Service providers can use it to reduce the fraction of unsuccessful recommendations, which, for example, decreases the likelihood that users develop notification blindness towards proactive recommendations.